Unified AI Security Posture Management Framework for Multi-Cloud Large Language Model Deployments

Abstract

The rapid proliferation of large language models across multi-cloud environments has introduced new challenges for enterprise security posture management. Existing cloud security posture management tools operate individually, leading to blind spots and delayed threat response for distributed AI workloads. This survey presents emerging Unified AI Security Posture Management (UAI-SPM) frameworks for hosting LLM applications in multi-cloud environments. This article aggregates recent developments in industry, regulatory frameworks, and technological trends, while also addressing needs in security tooling, essential framework components, and novel architectural patterns. The article describes the tradeoffs between autonomy and security in AI systems, provides a literature review for applying zero trust to generative AI systems, and makes evidence-based recommendations for enterprises deploying LLMs at scale. Key findings indicate that attacks against AI services are pervasive and that the majority of risk exposure stems from identity and access misconfigurations. In this context, integrated posture management frameworks that enable continuous discovery, behavioral insights, and adaptive compliance will be foundational to security resiliency in an AI-driven world. Organizations will also have to deal with accelerating regulatory scrutiny around continuous monitoring, transparent governance, and verifiable security controls throughout the AI system lifecycle.